The integration of GPUs into real-time systems introduces a range of fundamental challenges that threaten their ability to meet strict temporal guarantees. While GPUs excel at delivering high throughput through massive parallelism, these architectural strengths often conflict with the latency sensitivity and determinism demanded by real-time applications. Key obstacles include the opacity of closed-source drivers and implementation constraints that limit developer control over scheduling and synchronization, the non-deterministic nature of kernel execution and CPU–GPU communication that complicates worst-case execution time analysis, and severe resource contention that frequently leads to underutilization or unfair sharing of GPU resources. Additional difficulties arise from high power consumption, which restricts feasibility in energy-constrained platforms, and the considerable overhead of context switching, which disrupts temporal predictability. Together, these challenges manifest as missed deadlines, degraded reliability, and inefficient GPU utilization, underscoring the need for systematic research on new scheduling techniques, synchronization models, and hardware–software co-design strategies. In this chapter, we provide a concise overview of these challenges, analyzing their underlying causes and implications for the deployment of GPUs in real-time environments.